Cooling plants for large buildings and other facilities provide air conditioning of the interior space and include chillers, chilled water pumps, condensers, condenser water pumps, cooling towers with cooling tower fans, and air handling fans for distributing the cool air to the interior space. The drives for the pumps and fans may be variable or constant speed drives. Heating plants for such facilities include hot water boilers, hot water pumps, and air handling fans. The drives for these pumps and fans may also be variable or constant speed drives.
Global set point optimization is defined as the selection of the proper set points for chilled water supply, hot water supply, condenser water flow rate, tower fan air flow rate, and air handler discharge temperature that result in minimal total energy consumption of the chillers, boilers, chilled water pumps, condenser water pumps, hot water pumps, and air handling fans. Determining these optimal set points holds the key to substantial energy savings in a facility since the chillers, towers, boilers, pumps, and air handler fans together can comprise anywhere from 40% to 70% of the total energy consumption in a facility.
There has been study of the matter of determining optimal set points in the past. For example, in the article by Braun et al. 1989b. "Methodologies for optimal control of chilled water systems without storage", ASHRAE Transactions, Vol. 95, Part 1, pp. 652-62, they have shown that there is a strong coupling between optimal values of the chilled water and supply air temperatures; however, the coupling between optimal values of the chilled water loop and condenser water loop is not as strong. (This justifies the approach taken in the present invention of considering the chilled water loop and condenser water/cooling tower loops as separate loops and treating only the chiller, the chilled water pump, and air handler fan components to determine optimal .DELTA.T of the chilled water and air temperature across the cooling coil.)
It has also been shown that the optimization of the cooling tower loop can be handled by use of an open-loop control algorithm (Braun and Diderrich, 1990, "Performance and control characteristics of a large cooling system." ASHRAE Transactions, Vol. 93, Part 1, pp. 1830-52). They have also shown that a chance in wet bulb temperature has an insignificant influence on chiller plant power consumption and that near-optimal control of cooling towers for chilled water systems can be obtained from an algorithm based upon a combination of heuristic rules for tower sequencing and an open-loop control equation. This equation is a linear equation in only one variable, i.e., load, and correlates a near-optimal tower air flow in terms of load (part-load ratio). EQU G.sub.twr =1-.beta..sub.twr (PLR.sub.twr,cap -PLR) 0.25&lt;PLR&lt;1.0(1)
where
G.sub.twr =the tower air flow divided by the maximum air flow with all cells operating at high speed PA1 PLR=the chilled water load divided by the total chiller cooling capacity (part-load ratio) PA1 PLR.sub.twr,cap =value of PLR at which the tower operates at its capacity (G.sub.twr =) PA1 .beta..sub.twr =the slope of the relative tower air flow (G.sub.twr) versus the PLR function. PA1 Q.sub.a,max =m.sub.a,twr (h.sub.s,cwr -h.sub.s,i), sigma energy,h.sub.s,.sbsb.-- =h.sub.air,.sbsb.-- -.omega..sub.-- c.sub.pw T.sub.wb EQU Q.sub.w,max =m.sub.cw c.sub.pw (T.sub.cwr -T.sub.wb) ( 2) PA1 m.sub.a,twr =tower air flow rate PA1 m.sub.cw =condenser water flow rate PA1 T.sub.cwr =condenser water return temperature PA1 T.sub.wb =ambient air wet bulb temperature PA1 P.sub.comp =the power consumption of the chiller's compressor PA1 P.sub.pump =the power consumption of the chilled water pump PA1 .DELTA.T.sub.chw =the supply/return chilled water temperature PA1 K.sub.comp, K.sub.pump =constants, dependent on load
Estimates of these parameters may be obtained using design data and relationships presented in Table 1 below:
TABLE 1 __________________________________________________________________________ Parameter Estimates for Eqn. 1 Variable-Speed Parameter Single-Speed Fans Two-Speed Fans Fans __________________________________________________________________________ PLR.sub.twr,cap PLR.sub.0 1 #STR1## 2 #STR2## .beta..sub.twr 3 #STR3## 4 #STR4## 5 #STR5## 6 #STR6## where: 7 #STR7## 8 #STR8## (a.sub.twr,des + r.sub.twr,des) = the sum of the tower approach and range at design conditions __________________________________________________________________________
Once a near-optimal tower air flow is determined, Braun et al., 1987, "Performance and control characteristics of a large cooling system." ASHRAE Transactions, Vol. 93, Part 1, pp. 1830-52 have shown that for a tower with an effectiveness near unity, the optimal condenser flow is determined when the thermal capacities of the air and water are equal.
Cooling tower effectiveness is defined as: ##EQU1## where .epsilon.=effectiveness of cooling tower
A DDC controller can calculate the effectiveness, .epsilon., of the cooling tower, and if it is between 0.9 and 1.0 (Braun et al. 1987), m.sub.cw can be calculated from equating Q.sub.a,max and Q.sub.w,max once m.sub.a,twr is determined from Eqn. 1. Near-optimal operation of the condenser water flow and the cooling tower air flow can be obtained when variable speed drives are used for both the condenser water pumps and cooling tower fans.
Braun et al. (1989a. "Applications of optimal control to chilled water systems without storage." ASHRAE Transactions, Vol. 95, Part 1, pp. 663-75; 1989b. "Methodologies for optimal control of chilled water systems without storage", ASHRAE Transactions, Vol. 95, Part 1, pp. 652-62; 1987, "Performance and control characteristics of a large cooling system." ASHRAE Transactions, Vol. 93, Part 1, pp. 1830-52.) have done a number of pioneering studies on optimal and near-optimal control of chilled water systems. These studies involve application of two basic methodologies for determining optimal values of the independent control variables that minimize the instantaneous cost of chiller plant operation. These independent control variables are: 1) supply air set point temperature, 2) chilled water set point temperature, 3) relative tower air flow (ratio of the actual tower air flow to the design air flow), 4) relative condenser water flow (ratio of the actual condenser water flow to the design condenser water flow), and 5) the number of operating chillers.
One methodology uses component-based models of the power consumption of the chiller, cooling tower, condenser and chilled water pumps, and air handler fans. However, applying this method in its full generality is mathematically complex because it requires simultaneous solution of differential equations. In addition, this method requires measurements of power and input variables, such as load and ambient dry bulb and wet bulb temperatures, at each step in time. The capability of solving simultaneous differential equations is lacking in today's DDC controllers. Therefore, implementing this methodology in an energy management system is not practical.
Braun et al. (1987, 1989a, 1989b) also present an alternative, and somewhat simpler methodology for near-optimal control that involves correlating the overall system power consumption with a single function. This method allows a rapid determination of optimal control variables and requires measurements of only total power over a range of conditions. However, this methodology still requires the simultaneous solution of differential equations and therefore cannot practically be implemented in a DDC controller.
Optimal air-side and water-side control set points were identified by Hackner et al. (1985, "System Dynamics and Energy Use." ASHRAE Journal, June.) for a specific plant through the use of performance maps. These maps were generated by many simulations of the plant over the range of expected operating conditions. However, this procedure lacks generality and is not easily implemented in a DDC controller.
Braun et al. (1987) has suggested the use of a bi-quadratic equation to model chiller performance of the form: ##EQU2## where "x" is the ratio of the load to a design load, "y" is the leaving condenser water temperature minus the leaving chilled water temperature, divided by a design value, P.sub.ch is the actual chiller power consumption, and P.sub.des is the chiller power associated with the design conditions. The empirical coefficients of the above equation (a, b, c, d, e, f) are determined with linear least-squares curve-fitting applied to measured or modeled performance data. This model can be applied to both variable speed and constant speed chillers.
Kaya et al. (1983, "Chiller optimization by distributed control to save energy", Proceedings of the instrument Society of America Conference, Houston, Tex.) has used a component-based approach for modeling the power consumption of the chiller and chilled water pump under steady-state load conditions. In his paper, the chiller component power is approximated to be a linear function of the chilled water differential temperature, and chilled water pump component power to be proportional to the cube of the reciprocal of the chilled water differential temperature for each steady-state load condition. ##EQU3## where P.sub.Tot =the total power consumption
While the above described work allows the calculation of the optimal .DELTA.T.sub.chw, it lacks generality since the power consumption of the air handler fans is not considered in the analysis.
Accordingly, it is a primary object of the present invention to provide an improved digital controller for a cooling and heating plant that easily and effectively implements a near-optimal global set point control strategy.
A related object is to provide such an improved controller which enables a heating and/or cooling plant to be efficiently operated and thereby minimizes the energy costs involved in such operation.
Yet another object of the present invention is to provide such a controller that is adapted to provide approximate instantaneous cost savings information for a cooling or heating plant compared to a baseline operation.
A related object is to provide such a controller which provides accumulated cost savings information.
These and other objects of the present invention will become apparent upon reading the following detailed description while referring to the attached drawings.